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Description

A closed-loop universal multivariable optimizer is designed to enhance both the performance and quality of Model Predictive Control (MPC) systems. This optimizer utilizes data from Excel files sourced from Dynamic Matrix Control (DMC) by Aspen Tech, Robust Model Predictive Control Technology (RMPCT) from Honeywell, or Predict Pro from Emerson to develop and refine accurate models for various multivariable-controller variable (MV-CV) pairs. This innovative optimization technology eliminates the need for step tests typically required by Aspen Tech and Honeywell, operating entirely within the time domain while remaining user-friendly, compact, and efficient. Given that Model Predictive Controls (MPC) can encompass tens or even hundreds of dynamic models, the possibility of incorrect models is a significant concern. The presence of inaccurate dynamic models in MPCs leads to bias, which is identified as model prediction error, manifesting as discrepancies between predicted signals and actual measurements from sensors. COLUMBO serves as a powerful tool to enhance the accuracy of Model Predictive Control (MPC) models, effectively utilizing either open-loop or fully closed-loop data to ensure optimal performance. By addressing the potential for errors in dynamic models, COLUMBO aims to significantly improve overall control system effectiveness.

Description

ESMFold2 builds upon its predecessor, ESMFold, by establishing a new benchmark in single-sequence structure prediction and facilitating the creation of novel functional proteins via exploration of the latent space within the ESMC model. This advanced model is capable of forecasting high-resolution, all-atom 3D structures of biomolecular complexes straight from the amino acid sequence, and it allows for the incorporation of multiple sequence alignments to improve accuracy on difficult targets. Tailored for predicting structures through both sequence and structure modalities, it employs ESM representations that drive a series of looped folding layers while a diffusion model translates pairwise representations into atomic-resolution outcomes. ESMFold2 excels in predicting protein structures from amino acid sequences, providing detailed structural data, including precise all-atom coordinates for both backbone and side chains, along with confidence metrics and optional distogram predictions for in-depth structural evaluation. Furthermore, its innovative approach enhances the understanding of protein folding dynamics and functional implications, making it a valuable tool for researchers in the field.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Aspen DMC3
Biohub
Microsoft Excel
Python

Integrations

Aspen DMC3
Biohub
Microsoft Excel
Python

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

PiControl Solutions

Country

United States

Website

www.picontrolsolutions.com/products/columbo/

Vendor Details

Company Name

Biohub

Founded

2016

Country

United States

Website

biohub.ai/models/esmfold2

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